Citing StatProofBook

Instructions

If you cite The Book of Statistical Proofs in your scientific work, it is best practice to reference the Zenodo DOI (10.5281/zenodo.4305949) which always resolves the latest version of the StatProofBook. This could e.g. look as follows:

• Soch, Joram, et al. (2024). StatProofBook/StatProofBook.github.io: The Book of Statistical Proofs (Version 2023). Zenodo. https://doi.org/10.5281/ZENODO.4305949

Alternatively, you can also directly cite a proof from the archive in your article:

• [GitHub username] (YYYY). Proof: [Title of the proof]. The Book of Statistical Proofs, Proof #NNN. URL: https://statproofbook.github.io/P/[shortcut]; DOI: 10.5281/zenodo.4305949.

• majapavlo (2022). Proof: Probability density function of the log-normal distribution. The Book of Statistical Proofs, Proof #310. URL: https://statproofbook.github.io/P/lognorm-pdf; DOI: 10.5281/zenodo.4305949.

Alternatively, you may also include a footnote with the URL into your article:

• See: https://statproofbook.github.io/P/[shortcut]

• See: https://statproofbook.github.io/P/lognorm-pdf

Hall of Fame

Here is a list of scientific articles that have so far cited content from The Book of Statistical Proofs:

• Awad, P., Chan, J. H. H., Millon, M., Courbin, F., & Paic, E. (2023a). Probing compact dark matter objects with microlensing in gravitationally lensed quasars. Astronomy & Astrophysics, 673, A88. https://doi.org/10.1051/0004-6361/202245615

• Awad, P., Chan, J. H. H., Millon, M., Courbin, F., & Paic, E. (2023b). Probing compact dark matter objects with microlensing in gravitationally lensed quasars. https://doi.org/10.48550/ARXIV.2304.01320

• Bilton, M. A. (2022). Use of Surrogate Models for Continuous Optimal Experimental Design [Thesis, ResearchSpace@Auckland]. https://researchspace.auckland.ac.nz/handle/2292/61541

• Coupechoux, J.-F., Chierici, R., Hansen, H., Margueron, J., Somasundaram, R., & Sordini, V. (2023). Impact of O4 future detections on the determination of the dense matter equations of state. Physical Review D, 107(12), 124006. https://doi.org/10.1103/PhysRevD.107.124006

• Dam, T., Stenger, P., Schneider, L., Pajarinen, J., D’Eramo, C., & Maillard, O.-A. (2023). Monte-Carlo tree search with uncertainty propagation via optimal transport. https://doi.org/10.48550/ARXIV.2309.10737

• de la Torre, J. (2023). Autocodificadores Variacionales (VAE) Fundamentos Teóricos y Aplicaciones. https://doi.org/10.48550/ARXIV.2302.09363

• Děd, T. (2023). Konstrukce modelu pro překlad záznamu znakového jazyka s využitím neuronových sítí. https://dspace.cvut.cz/handle/10467/111309

• Fajar, M., Setiawan, & Iriawan, N. (2023). The Adjusted SNR and It’s Application for Selection Lorenz Function of Income Inequality Analysis. Procedia Computer Science, 227, 1–16. https://doi.org/10.1016/j.procs.2023.10.497

• Görner, M., Dicke, P. W., & Thier, P. (2023). Is there a brain area dedicated to socially guided spatial attention? [Preprint]. Neuroscience. https://doi.org/10.1101/2023.01.20.524674

• Heußen, S., Winter, D., Rispler, M., & Müller, M. (2023). Dynamical subset sampling of quantum error correcting protocols. https://doi.org/10.48550/ARXIV.2309.12774

• Ivănescu, L., & O’Neill, N. T. (2023). Multi-star calibration in starphotometry. Atmospheric Measurement Techniques, 16(24), 6111–6121. https://doi.org/10.5194/amt-16-6111-2023

• Larsen, A. H. (2023). Fitting multiple small-angle scattering datasets simultaneously: On the optimal use of priors and weights. https://doi.org/10.48550/ARXIV.2311.06408

• Liu, X., Yuan, J., An, B., Xu, Y., Yang, Y., & Huang, F. (2023). C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder. https://doi.org/10.48550/ARXIV.2310.17325

• Loukas, O., & Chung, H. R. (2023). Total Empiricism: Learning from Data. https://doi.org/10.48550/ARXIV.2311.08315

• Mulder, E. (2023). Fast square-free decomposition of integers using class groups. https://doi.org/10.48550/ARXIV.2308.06130

• Mustapa, N. A., Senawi, A., & Liang, C. Z. (2023). Feature Selection Using Law of Total Variance with Fast Correlation-Based Filter. 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), 35–40. https://doi.org/10.1109/ICSECS58457.2023.10256367

• Mustapa, N. A., Senawi, A., & Wei, H.-L. (2023). Supervised Feature Selection based on the Law of Total Variance. MEKATRONIKA, 5(2), 100–110. https://doi.org/10.15282/mekatronika.v5i2.9998

• Öz, H. N. (2023, September 26). New Risk Measures: Magnitude and Propensity Approach. https://thesis.unipd.it/handle/20.500.12608/52276

• Özkaya, E., Rottmayer, J., & Gauger, N. R. (2024). Gradient Enhanced Surrogate Modeling Framework for Aerodynamic Design Optimization. In AIAA SCITECH 2024 Forum. https://doi.org/10.2514/6.2024-2670

• Šimon, S. (2022). Metody návrhu experimentů pro tvorbu zjednodušeného modelu okraje plasmatu [B.S. thesis, České vysoké učení technické v Praze. Vypočetní a informační centrum.]. https://dspace.cvut.cz/handle/10467/101041

• Smith, I., Ortmann, J., Abbas-Aghababazadeh, F., Smirnov, P., & Haibe-Kains, B. (2023). On the distribution of cosine similarity with application to biology. https://doi.org/10.48550/ARXIV.2310.13994

• Soch, J. (2020). Distributional Transformation Improves Decoding Accuracy When Predicting Chronological Age From Structural MRI. Frontiers in Psychiatry, 11, 604268. https://doi.org/10.3389/fpsyt.2020.604268

• Soch, J. (2023). Searchlight-based trial-wise fMRI decoding in the presence of trial-by-trial correlations [Preprint]. Neuroscience. https://doi.org/10.1101/2023.12.05.570090

• Soch, J., Richter, A., Schott, B. H., & Kizilirmak, J. M. (2022). A novel approach for modelling subsequent memory reports by separating decidedness, recognition and confidence [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/u5t82

• Subramonian, A., Sagun, L., Chang, K.-W., & Sun, Y. (2022). Group Excess Risk Bound of Overparameterized Linear Regression with Constant-Stepsize SGD. Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022. https://openreview.net/forum?id=TRpJAAK3o0X

• Vinaroz, M., & Park, M. (2021). Differentially private stochastic expectation propagation (DP-SEP). https://doi.org/10.48550/ARXIV.2111.13219

• Vinaroz, M., & Park, M. (2022). Differentially Private Stochastic Expectation Propagation. Transactions on Machine Learning Research. https://openreview.net/forum?id=e5ILb2Nqst

• Zeng, H., Lyu, H., Hu, D., Xia, Y., & Luo, J. (2023). Mixture of Weak & Strong Experts on Graphs. https://doi.org/10.48550/ARXIV.2311.05185

retrieved from: Google Scholar; last update: 2024-01-12